Distributed power distribution cabinet cluster power collaborative distribution method and system

Abstract

The application belongs to the technical field of power distribution, and particularly relates to a distributed power distribution cabinet cluster power collaborative distribution method and system. The method obtains the node admittance matrix of the power distribution cabinet cluster, divides the power distribution cabinet cluster into multiple partitions by using a spectral clustering algorithm, obtains the power imbalance adjustment amount borne by each partition by interacting the total power, load prediction and exchange constraint of each partition node through a distributed consistency algorithm, takes the local load change rate of any power distribution cabinet, the residual power of the power distribution cabinet and the power imbalance adjustment amount of the partition to which the power distribution cabinet belongs as input variables, determines the adaptive active power-frequency droop coefficient of the power distribution cabinet, corrects the frequency reference value according to the power imbalance adjustment amount, and makes all power distribution cabinets in the partition execute droop control based on the corrected frequency reference value and the determined adaptive active power-frequency droop coefficient of each power distribution cabinet, so as to realize collaborative distribution of power. The application realizes stable, economic and reliable power collaborative distribution of the power distribution cabinet cluster.

Description

Technical Field

[0001] This invention belongs to the field of power distribution technology, specifically relating to a method and system for collaborative power allocation of distributed distribution cabinet clusters. Background Technology

[0002] With the increasing penetration of distributed energy, energy storage systems, and electric vehicle charging stations in power distribution networks, cluster systems composed of numerous intelligent distribution cabinets have become crucial for achieving flexible regional energy regulation and autonomous management. Against this backdrop, how to efficiently and stably coordinate the power distribution among numerous distributed units within the cluster has become a core technological challenge in the field of power distribution network operation and control.

[0003] Currently, the main technologies applied to distributed power allocation are droop control and its improved versions. Traditional droop control simulates the droop characteristics of synchronous generators, enabling each power unit to autonomously adjust its active and reactive power output based on locally measured frequency or voltage deviations. It features plug-and-play functionality without the need for high-speed communication. However, traditional droop control has inherent drawbacks: due to differences in line impedance, it cannot achieve power allocation proportional to capacity; steady-state frequency deviations occur after load changes; and using a fixed droop coefficient, it cannot adaptively adjust based on the real-time operating status of each distribution cabinet and network changes.

[0004] To overcome the inherent shortcomings of traditional droop control, secondary control schemes have been developed in existing technologies. These schemes can be categorized into centralized and distributed types. Centralized control relies on a central controller to collect global information and issue commands, but it suffers from single-point-of-failure risks and communication bandwidth pressure. Distributed collaborative control achieves global objectives in a distributed manner through information exchange between neighboring units, improving reliability and scalability. However, existing distributed methods still face challenges when handling large-scale distribution cabinet clusters. On the one hand, they neglect the differences in electrical coupling between nodes, failing to form partitions that match the topology characteristics, resulting in poor control efficiency and stability. On the other hand, the tuning of adaptive droop coefficients often focuses on single state variables, making it difficult to optimize power distribution between and within partitions while ensuring frequency stability. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a method and system for coordinated power allocation of distributed power distribution cabinet clusters, so as to solve the problems of poor control efficiency, frequency stability and operating economy caused by the neglect of network electrical coupling characteristics and failure to combine the comprehensive state of each unit when the existing distributed control method deals with large-scale power distribution cabinet clusters.

[0006] To solve the above problems, the technical solution of the distributed power distribution cabinet cluster collaborative allocation method provided by the present invention is as follows:

[0007] A method for coordinated power allocation in a distributed power distribution cabinet cluster includes the following steps:

[0008] Obtain the node admittance matrix of the distribution cabinet cluster. Based on the electrical coupling degree represented by the node admittance matrix, use the spectral clustering algorithm to divide the distribution cabinet cluster into multiple partitions. Within each partition, calculate the comprehensive operating cost index based on the remaining power, health status and line loss prediction of each distribution cabinet. Select the distribution cabinet with the lowest comprehensive operating cost index as the reference distribution cabinet for that partition.

[0009] In a higher-level control architecture, each partition is treated as a whole processing node. The total power, load forecast, and exchange constraints of each partition node are exchanged through a distributed consensus algorithm to calculate the power imbalance adjustment amount undertaken by each partition.

[0010] For any distribution cabinet in the distribution cabinet cluster, the local load change rate, its own remaining power and the power imbalance adjustment amount of the partition to which it belongs are used as input variables. The preset mapping relationship is queried to determine the adaptive active power-frequency droop coefficient of the distribution cabinet.

[0011] Within each zone, the reference distribution cabinet corrects the frequency reference value based on the power imbalance adjustment amount. All distribution cabinets within the zone perform droop control based on the corrected frequency reference value and their respective determined adaptive active power-frequency droop coefficients to achieve coordinated power distribution.

[0012] Furthermore, the steps of dividing the distribution cabinet cluster into multiple zones include:

[0013] An undirected weighted graph of the distribution cabinet cluster is constructed based on the node admittance matrix. Each node in the undirected weighted graph represents a distribution cabinet, and the weight of the edge connecting two nodes is the modulus of the admittance value between the two nodes.

[0014] Calculate the Laplacian matrix of the undirected weighted graph;

[0015] The Laplacian matrix is ​​decomposed into eigenvalues, and the eigenvectors corresponding to the smallest non-zero eigenvalues ​​of the preset number of partitions are extracted. Each distribution cabinet is regarded as a data point in a multi-dimensional space composed of these eigenvectors, and the k-means clustering algorithm is used to cluster all data points to obtain multiple partitions.

[0016] Furthermore, the method for calculating the Laplace matrix is ​​as follows:

[0017] Construct a similarity matrix with the absolute values ​​of the elements in the node admittance matrix as weights. The diagonal elements in the similarity matrix are 0, and the remaining elements correspond to the weight values ​​of the edge connecting two nodes in the undirected weighted graph. If there is no direct connection between two nodes, the corresponding element value is 0.

[0018] Calculate the degree matrix corresponding to the similarity matrix. All off-diagonal elements in the degree matrix are 0, and the diagonal elements represent the sum of the weights of all edges connected to the node.

[0019] Subtracting the similarity matrix from the degree matrix yields the Laplace matrix.

[0020] Furthermore, the comprehensive operating cost index is composed of a weighted sum of three parts: remaining power cost, health status cost, and line loss cost. The remaining power cost is inversely proportional to the normalized remaining power value of the distribution cabinet; the health status cost is inversely proportional to the normalized health status value of the distribution cabinet; and the line loss cost is the normalized average estimated line loss from this distribution cabinet to all other distribution cabinets in the same zone, which is obtained by dividing the average estimated line loss power by the rated power of the distribution cabinet.

[0021] Preferably, the remaining power cost is obtained by calculating the reciprocal of the normalized remaining power value of the distribution cabinet; the health status cost is obtained by calculating the reciprocal of the normalized health status value of the distribution cabinet.

[0022] Furthermore, the method for determining the adaptive active-frequency droop coefficient of any distribution cabinet includes:

[0023] A preset three-input, single-output fuzzy logic controller is used to realize the preset mapping relationship. The local load change rate of the distribution cabinet, its own remaining power, and the power imbalance adjustment amount of the zone to which it belongs are used as the three inputs of the fuzzy controller. The output value of the fuzzy logic controller is the active power-frequency droop coefficient of the distribution cabinet.

[0024] Preferably, the control rules of the fuzzy controller are set as follows: when the local load change rate is positive, its own remaining power is high, and the power imbalance adjustment amount of its partition is positive, the output active-frequency droop coefficient is small; when the local load change rate is negative, its own remaining power is low, and the power imbalance adjustment amount of its partition is negative, the output active-frequency droop coefficient is large.

[0025] Furthermore, the corrected frequency reference value is calculated using the following formula:

[0026] ;

[0027] in, This is the corrected frequency reference value; The rated frequency of the power grid. The preset frequency adjustment gain coefficient, This is the power imbalance adjustment amount.

[0028] Furthermore, the formula for calculating the active power output of each distribution cabinet within the zone is as follows:

[0029] ;

[0030] in, For the first in the partition The active power output of each distribution cabinet; For the first in the partition The base power of each distribution cabinet; The actual frequency of the power grid. For the first in the partition The active power-frequency droop coefficient of each distribution cabinet. To ensure the stability and physical feasibility of the control system, the value of the active power-frequency droop coefficient is limited to a real number that is always greater than zero.

[0031] The technical solution of the distributed power distribution cabinet cluster power collaborative allocation system provided by this invention is as follows:

[0032] A distributed power distribution cabinet cluster power collaborative allocation system includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the distributed power distribution cabinet cluster power collaborative allocation method described in any of the above technical solutions.

[0033] The beneficial effects of this invention are as follows:

[0034] Compared with existing technologies, this invention rationally partitions the distribution cabinet cluster based on its electrical coupling characteristics, matching the control architecture with the network topology and improving the stability and response efficiency of large-scale cluster control. By constructing a two-layer collaborative control framework, the upper layer achieves distributed coordination of power balance between partitions, while the lower layer allocates power within each partition based on the comprehensive operating cost of each distribution cabinet. By combining the upper-layer partition adjustment commands with the lower-layer distribution cabinet's own state and local load conditions, the power output contribution of each unit is jointly determined. This not only restores the system frequency but also ensures that power can be allocated on demand and economically at both the global and local levels, improving the operational economy, reliability, and collaborative control level of the entire distribution cabinet cluster. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the steps of the distributed power distribution cabinet cluster collaborative allocation method of the present invention.

[0036] Figure 2 This is a schematic diagram showing the selection of reference distribution cabinets within a given zone. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0038] Specific embodiments of the distributed power distribution cabinet cluster power collaborative allocation method provided by the present invention:

[0039] like Figure 1 As shown, the distributed power distribution cabinet cluster power collaborative allocation method includes the following steps:

[0040] S1. Obtain the node admittance matrix of the distribution cabinet cluster. Based on the electrical coupling degree represented by the node admittance matrix, use the spectral clustering algorithm to divide the distribution cabinet cluster into multiple partitions. Within each partition, calculate the comprehensive operating cost index based on the remaining power, health status and line loss prediction of each distribution cabinet. Select the distribution cabinet with the lowest comprehensive operating cost index as the reference distribution cabinet for that partition.

[0041] This step improves control stability and response efficiency by decomposing the complex distribution cabinet cluster system into smaller, more manageable subsystems, or partitions. The node admittance matrix is ​​used to quantify the electrical connectivity between distribution cabinets within a partition. Based on this, a spectral clustering algorithm is employed to group electrically connected distribution cabinets into the same partition. This ensures that the distribution cabinets within a partition are highly correlated in electrical characteristics, making collaborative control within that partition more efficient and stable, and achieving a match between the control architecture and the network physical topology. A reference distribution cabinet is established in each partition to receive upper-level commands and coordinate power distribution within the partition. This simplifies the control logic and avoids all distribution cabinets in each partition participating in upper-level coordination, thus reducing communication burden. The comprehensive operating cost index comprehensively considers the remaining power capacity, health status, and line loss prediction of the distribution cabinets, such as… Figure 2 As shown, distribution cabinet G4 has the lowest overall operating cost index. Therefore, distribution cabinet G4 is selected as the reference distribution cabinet. This means that the equipment with the best current operating status and the lowest operating cost is selected to undertake the coordination task, which helps to improve the operating economy and reliability of the entire zone.

[0042] Specifically, the steps for dividing the power distribution cabinet cluster into multiple partitions include:

[0043] An undirected weighted graph of the distribution cabinet cluster is constructed based on the node admittance matrix. Each node in the undirected weighted graph represents a distribution cabinet, and the weight of the edge connecting two nodes is the modulus of the admittance value between the two nodes.

[0044] Calculate the Laplacian matrix of the undirected weighted graph;

[0045] Eigenvalue decomposition is performed on the Laplace matrix to extract the eigenvectors corresponding to the smallest non-zero eigenvalues ​​of the preset number of partitions. Each distribution cabinet is regarded as a data point in a multi-dimensional space composed of these eigenvectors. The k-means clustering algorithm is used to cluster all data points, and distribution cabinets with close electrical connections are divided into the same partition, thus obtaining multiple partitions.

[0046] More specifically, a power grid topology model of the distribution cabinet cluster is first established, and the node admittance matrix is ​​calculated based on the line parameters. "Undirected" means that the connection between two nodes is bidirectional and has no directionality. "Weighted" means that the edge connecting two nodes has a numerical value, which is used to represent the tightness of the connection. In practical applications, a similarity matrix is ​​constructed with the absolute values ​​of the elements of the node admittance matrix as weights. The similarity matrix is ​​a direct digital representation of the undirected weighted graph. Each element in the similarity matrix corresponds to the weight value of the edge connecting two nodes in the undirected weighted graph. If there is no direct connection between two nodes, the corresponding element has a value of 0. The corresponding degree matrix is ​​calculated based on the similarity matrix. The elements on the diagonal of the degree matrix represent the sum of the weights of all edges connecting to a node, which measures the total connection strength of that node in the entire network. The Laplace matrix is ​​obtained by subtracting the similarity matrix from the degree matrix.

[0047] In step S1, the comprehensive operating cost index is composed of a weighted sum of three parts: remaining power cost, health status cost, and line loss cost. The remaining power cost is inversely proportional to the normalized remaining power value of the distribution cabinet; the health status cost is inversely proportional to the normalized health status value of the distribution cabinet. In this embodiment, the remaining power cost is obtained by calculating the reciprocal of the normalized remaining power value of the distribution cabinet; the health status cost is obtained by calculating the reciprocal of the normalized health status value of the distribution cabinet. The line loss cost is the normalized average estimated line loss from the distribution cabinet to all other distribution cabinets within the same zone, obtained by calculating the ratio of the average estimated line loss power to the rated power of the distribution cabinet. Here, the average estimated line loss power is estimated within a certain zone, centered on a single distribution cabinet, as the average power lost on the line due to resistance when the distribution cabinet exchanges power with all other distribution cabinets within that zone. Since the actual line loss changes constantly with real-time load and power generation, it cannot be known accurately in advance. Therefore, this invention adopts a predictive approach to estimate a representative, average loss value based on line impedance and historical power data.

[0048] S2, in a higher-level control architecture, treats each partition as a whole processing node. Through a distributed consensus algorithm, it interacts with each partition node on the total power, load prediction, and exchange constraints, and calculates the power imbalance adjustment amount undertaken by each partition.

[0049] In practical applications, when multiple charging stations in a region start up simultaneously, a significant local power deficit can occur. The core objective of this step is to collaboratively resolve the power imbalance problem across the entire distribution cabinet cluster. When the overall power generation of the distribution cabinet cluster does not match the load, it is necessary to determine how much regulation work each zone should undertake. Higher-level control architectures no longer focus on the specific situation of each distribution cabinet, but rather manage each zone as a whole. This hierarchical control architecture greatly simplifies the management complexity of large-scale clusters. Upper-level controllers only need to coordinate with a limited number of zones, without directly communicating with hundreds or thousands of lower-level devices, thus improving system scalability.

[0050] Specifically, each partition aggregates the total generating power and total load power of all distribution cabinets within it to calculate the initial power imbalance of that partition. In a higher-level control architecture, each partition acts as a processing node, exchanging information only with its neighboring partition nodes. A distributed consensus algorithm is used for communication and negotiation. During the negotiation process, each partition node interacts with its neighboring nodes, including its current total power, predicted future load values, and power exchange constraints with other partitions. Through distributed iterative calculations, all partition nodes can reach a consensus on the overall power imbalance of the entire distribution cabinet cluster and, based on a preset allocation strategy, calculate their respective share of regulation responsibility, i.e., the power imbalance regulation amount.

[0051] S3. For any distribution cabinet in the distribution cabinet cluster, the local load change rate, its own remaining power, and the power imbalance adjustment amount of the partition to which it belongs are used as input variables. The preset mapping relationship is queried to determine the adaptive active power-frequency droop coefficient of the distribution cabinet.

[0052] The purpose of this step is to overcome the shortcomings of traditional droop control, which uses a fixed droop coefficient and cannot adaptively adjust according to the real-time status of the distribution cabinet and network changes. By dynamically adjusting the active power-frequency droop coefficient, the power contribution of each distribution cabinet can be made more reasonable and economical. In this step, a preset three-input, single-output fuzzy logic controller is used to realize the preset mapping relationship. The local load change rate of the distribution cabinet, its own remaining power, and the power imbalance adjustment amount of its zone are used as the three inputs of the fuzzy logic controller, and the output value of the fuzzy logic controller is the active power-frequency droop coefficient of the distribution cabinet.

[0053] The fuzzy controller's control rules are set as follows: when the local load change rate is positive, its own remaining power is high, and the power imbalance adjustment of its zone is positive, the output active-frequency droop coefficient is small; when the local load change rate is negative, its own remaining power is low, and the power imbalance adjustment of its zone is negative, the output active-frequency droop coefficient is large. In this control rule, a positive local load change rate means that the local electrical load is increasing rapidly, and a negative local load change rate means that the local electrical load is decreasing rapidly. A positive power imbalance adjustment of its zone means that the zone has a severe power deficit, requiring each unit within the zone to significantly increase its power output to compensate; a negative power imbalance adjustment means that the zone has a severe power surplus, requiring each unit within the zone to significantly reduce its power output.

[0054] When local load demand increases sharply, the unit has sufficient power and the zone needs to output a large amount of power, a smaller active-frequency droop factor is output to enable it to handle more power; conversely, when local load decreases sharply, the unit has insufficient power and the zone needs to absorb a large amount of power, a larger active-frequency droop factor is output to reduce its power response.

[0055] S4. Within each zone, the reference distribution cabinet corrects the frequency reference value according to the power imbalance adjustment amount. All distribution cabinets within the zone perform droop control based on the corrected frequency reference value and their respective determined adaptive active power-frequency droop coefficients to achieve coordinated power distribution.

[0056] This step is the final power allocation execution stage of the entire collaborative allocation method, and it consists of two parts: frequency adjustment and droop control. The reference distribution cabinet selected in step S1 actively corrects the frequency reference value based on the power imbalance adjustment amount allocated to that zone calculated in step S2, restoring the system frequency to near its rated value. This avoids drastic fluctuations in the grid frequency caused by large-scale charging loads and solves the problem of steady-state frequency deviation caused by traditional droop control. Each distribution cabinet in each zone then autonomously adjusts its active power output based on the corrected frequency reference value and its own determined adaptive active-frequency droop coefficient.

[0057] Specifically, the corrected frequency reference value is calculated using the following formula:

[0058] ;

[0059] in, This is the corrected frequency reference value; The rated frequency of the power grid. The preset frequency adjustment gain coefficient, This is the power imbalance adjustment amount.

[0060] The formula for calculating the active power output of each distribution cabinet within the zone is as follows:

[0061] ;

[0062] in, For the first in the partition The active power output of each distribution cabinet; The actual frequency of the power grid can be measured in real time by each distribution cabinet within the zone; For the first in the partition The active-frequency droop factor of each distribution cabinet; For the first in the partition The reference power of a distribution cabinet represents the active power output of the distribution cabinet under ideal conditions, that is, when the actual frequency of the power grid is exactly equal to the corrected reference frequency.

[0063] Specific embodiments of the distributed power distribution cabinet cluster collaborative allocation system provided by the present invention:

[0064] The distributed power distribution cabinet cluster power collaborative allocation system includes a processor and a memory. The memory stores a computer program. When the computer program instructions are executed by the processor, the distributed power distribution cabinet cluster power collaborative allocation method in the above embodiments can be implemented.

[0065] The distributed power distribution cabinet cluster power collaborative allocation system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0066] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0067] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A distributed power distribution cabinet cluster power collaborative distribution method, characterized in that, Includes the following steps: Obtain the node admittance matrix of the distribution cabinet cluster. Based on the electrical coupling degree represented by the node admittance matrix, use the spectral clustering algorithm to divide the distribution cabinet cluster into multiple partitions. Within each partition, calculate the comprehensive operating cost index based on the remaining power, health status and line loss prediction of each distribution cabinet. Select the distribution cabinet with the lowest comprehensive operating cost index as the reference distribution cabinet for that partition. The comprehensive operating cost index is composed of the weighted sum of three parts: remaining power cost, health status cost, and line loss cost. The remaining power cost is inversely proportional to the normalized remaining power value of the distribution cabinet; the health status cost is inversely proportional to the normalized health status value of the distribution cabinet. The line loss cost is the normalized average estimated line loss from this distribution cabinet to all other distribution cabinets in the same zone, which is obtained by dividing the average estimated line loss power by the rated power of the distribution cabinet. In a higher-level control architecture, each partition is treated as a whole processing node. The total power, load forecast and exchange constraints of each partition node are exchanged through a distributed consensus algorithm to calculate the power imbalance adjustment amount undertaken by each partition, that is, the adjustment responsibility share that each should bear. For any distribution cabinet in the distribution cabinet cluster, the local load change rate, its own remaining power and the power imbalance adjustment amount of the partition to which it belongs are used as input variables. The preset mapping relationship is queried to determine the adaptive active power-frequency droop coefficient of the distribution cabinet. This includes: using a preset three-input, single-output fuzzy logic controller to realize the preset mapping relationship. The three pieces of information—the local load change rate of the distribution cabinet, its own remaining power, and the power imbalance adjustment amount of its zone—are used as the three inputs to the fuzzy controller. The output value of the fuzzy logic controller is the active power-frequency droop coefficient of the distribution cabinet. The control rules of the fuzzy controller are set as follows: when the local load change rate is positive and large, the remaining power is high, and the power imbalance adjustment amount of its zone is positive and large, the output active power-frequency droop coefficient is small; when the local load change rate is negative and large, the remaining power is low, and the power imbalance adjustment amount of its zone is negative and large, the output active power-frequency droop coefficient is large. A positive local load change rate indicates a rapid increase in local electricity load; conversely, a negative rate indicates a rapid decrease. A positive power imbalance adjustment rate for a given zone indicates a severe power deficit in that zone, requiring a significant increase in power output from all units within the zone to compensate; conversely, a negative rate indicates a severe power surplus in that zone, requiring a significant reduction in power output from all units within the zone. Within each zone, the reference distribution cabinet corrects the frequency reference value based on the power imbalance adjustment. All distribution cabinets within the zone perform droop control based on the corrected frequency reference value and their respective determined adaptive active power-frequency droop coefficients to achieve coordinated power distribution.

2. The distributed power distribution cabinet cluster power collaborative distribution method according to claim 1, characterized in that, The steps to divide a power distribution cabinet cluster into multiple partitions include: An undirected weighted graph of the distribution cabinet cluster is constructed based on the node admittance matrix. Each node in the undirected weighted graph represents a distribution cabinet, and the weight of the edge connecting two nodes is the modulus of the admittance value between the two nodes. Calculate the Laplacian matrix of the undirected weighted graph; The Laplacian matrix is ​​decomposed into eigenvalues, and the eigenvectors corresponding to the smallest non-zero eigenvalues ​​of the preset number of partitions are extracted. Each distribution cabinet is regarded as a data point in a multi-dimensional space composed of these eigenvectors, and the k-means clustering algorithm is used to cluster all data points to obtain multiple partitions.

3. The distributed power distribution cabinet cluster power collaborative allocation method according to claim 2, characterized in that, The method for calculating the Laplace matrix is ​​as follows: Construct a similarity matrix with the absolute values ​​of the elements in the node admittance matrix as weights. The diagonal elements in the similarity matrix are 0, and the remaining elements correspond to the weight values ​​of the edge connecting two nodes in the undirected weighted graph. If there is no direct connection between two nodes, the corresponding element value is 0. Calculate the degree matrix corresponding to the similarity matrix. All off-diagonal elements in the degree matrix are 0, and the diagonal elements represent the sum of the weights of all edges connected to the node. Subtracting the similarity matrix from the degree matrix yields the Laplace matrix.

4. The distributed power distribution cabinet cluster power collaborative allocation method according to claim 1, characterized in that, The remaining power cost is obtained by calculating the reciprocal of the normalized remaining power value of the distribution cabinet; the health status cost is obtained by calculating the reciprocal of the normalized health status value of the distribution cabinet.

5. The distributed power distribution cabinet cluster power collaborative allocation method according to claim 1, characterized in that, The corrected frequency reference value is calculated using the following formula: ； in, This is the corrected frequency reference value; The rated frequency of the power grid. The preset frequency adjustment gain coefficient, This is the power imbalance adjustment amount.

6. The distributed power distribution cabinet cluster power collaborative allocation method according to claim 5, characterized in that, The formula for calculating the active power output of each distribution cabinet within the zone is as follows: ； in, For the first in the partition The active power output of each distribution cabinet; For the first in the partition The base power of each distribution cabinet; The actual frequency of the power grid. For the first in the partition The active power-frequency droop factor of each distribution cabinet.

7. A distributed power distribution cabinet cluster power collaborative allocation system, characterized in that, It includes a processor and a memory, the memory storing a computer program, which, when executed by the processor, implements the distributed power distribution cabinet cluster power collaborative allocation method according to any one of claims 1-6.

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